GPT-5.6 Sol vs Muse Ember
GPT-5.6 Sol leads Muse Ember on coding, 70.6 to 64.9 on the Vibe Coding Index, and Muse Ember costs 40.0x less per output token ($0.50 vs $20.00 per million). Updated November 14, 2026; 9 benchmarks have a result for both.
vs boards won of 9 shared
GPT-5.6 Sol or Muse Ember: which to pick
Pick GPT-5.6 Sol for coding: it scores 70.6 on the Vibe Coding Index against 64.9 for Muse Ember.
Pick Muse Ember for output tokens 40.0x cheaper ($0.50 vs $20.00 per million) and work measured by Terminal-Bench 4 (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
Model AGPT-5.6 SolOpenAI70.6/1005 independent benchmarks$1.20 per solved taskModel BMuse EmberMeta64.9/1003 independent benchmarksNo cost per solved task
- Shared results
- 9
- GPT-5.6 Sol only
- 142
- Muse Ember only
- 6
- Like-for-like categories
- 3 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GPT-5.6 Sol
GPT-5.6 Sol scores 70.6 against 64.9.
5 and 3 benchmarks behind the scores
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 47.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.6 Sol has a Design Arena: websites result, 1319.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.6 Sol $0.014, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol 70.6 from 5 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Reasoning, 21.0 points apart on average over 1 shared benchmark.
- Widest single gap
- CritPt v2, hard subset (Artificial Analysis run): GPT-5.6 Sol 60.0% against Muse Ember 39.0%, 21.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Muse Ember $0.50 per million output tokens, 40.0x apart.
- Context window
- GPT-5.6 Sol takes 1.05M tokens, Muse Ember 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.6 Sol | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 0 | ||
| All shared boards | 3 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1615 | 1609 | GPT-5.6 Sol |
| AA Coding Index | 78.3 | Not measured | Only GPT-5.6 Sol measured |
| LiveCodeBench (Vals AI) | 82.6% | Not measured | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | 96.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | 37.9% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 80.5% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 52.9% | Not measured | Only GPT-5.6 Sol measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | Not measured | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 39.9% | 40.9% | Muse Ember |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1495 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | 1487 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1618 | 1616 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1612 | 1621 | Muse Ember |
| LMArena WebDev: from an image | 1606 | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| SWE-Bench Pro V2 | 95.5% | Not measured | Only GPT-5.6 Sol measured |
| SWE Atlas: Codebase QnA | 46.0% | Not measured | Only GPT-5.6 Sol measured |
| SWE Atlas: Test Writing | 45.9% | Not measured | Only GPT-5.6 Sol measured |
| GameBench 2 | 80.1 | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 3.0 | 34.6% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4.0 | 37.3% | Not measured | Only GPT-5.6 Sol measured |
| CursorBench 4.0 | 41.7% | Not measured | Only GPT-5.6 Sol measured |
| ProgramBench | 1.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 163.0 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 47.4 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building web apps | 1218 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | 1233 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1223 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | 1219 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | 1237 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | 1208 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1173 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1205 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: spreadsheets | 1152 | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | Not measured | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | 31.1% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 48.1% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| Excel Modeling Benchmark (Vals AI) | 72.3% | Not measured | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | Not measured | Only GPT-5.6 Sol measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 31.0% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1258 | Not measured | Only GPT-5.6 Sol measured |
| MCP Atlas | 81.8% | Not measured | Only GPT-5.6 Sol measured |
| τ³-Banking | 46.9% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 14 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | Not measured | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | Not measured | Only GPT-5.6 Sol measured |
| HLE-Diamond (Artificial Analysis run) | Not measured | 25.1% | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | 60.0% | 39.0% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | Not measured | Only GPT-5.6 Sol measured |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.6 Sol measured |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: expert | 1516 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | 1479 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | 1306 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1306 | Not measured | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Scale run) | 31.2% | Not measured | Only GPT-5.6 Sol measured |
| GoBench | 1928 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: math | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1493 | Not measured | Only GPT-5.6 Sol measured |
| MathArena | 56.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | 64.4% | Not measured | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 74.8% | Not measured | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| GeoBench | 73.0% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: science | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | 1430 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | 1475 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1458 | Not measured | Only GPT-5.6 Sol measured |
| FACTS Benchmark Suite | 70.6% | Not measured | Only GPT-5.6 Sol measured |
| PRBench Finance | 50.4% | Not measured | Only GPT-5.6 Sol measured |
| PRBench Legal | 50.5% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1319 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: UI components | 1327 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: game dev | 1364 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: data viz | 1321 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: 3D | 1368 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: all code categories | 1338 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: SVG | 1304 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index36 boards, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | Not measured | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | Not measured | Only GPT-5.6 Sol measured |
| GDPval-AA | 1619 | Not measured | Only GPT-5.6 Sol measured |
| DTBench | 95.7% | Not measured | Only GPT-5.6 Sol measured |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | 1451 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision | 1282 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | 1450 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | 1525 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: German | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1471 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1448 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1474 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1445 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | 1463 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1462 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | 1480 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | 1464 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | 1448 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: English | 1284 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1311 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1247 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1285 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1480 | Not measured | Only GPT-5.6 Sol measured |
| EQ-Bench 4 | 1236 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-5.6 Sol$0.014Fits in one request
Muse Ember$0.0003Fits in one request
Muse Ember costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Muse Ember$0.0065Fits in one request
Muse Ember costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Muse Ember$0.025Fits in one request
Muse Ember costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.6 Sol | Meta Muse Ember |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | November 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $4.00 | $0.10 |
| Output $/Mtok | $20.00 | $0.50 |
| Coding index | 70.6 | 64.9 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $1.20 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Muse Ember for coding?
Pick GPT-5.6 Sol for coding: it scores 70.6 on the Vibe Coding Index against 64.9 for Muse Ember. Pick Muse Ember for output tokens 40.0x cheaper ($0.50 vs $20.00 per million) and work measured by Terminal-Bench 4 (Vals AI) or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, GPT-5.6 Sol or Muse Ember?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GPT-5.6 Sol or Muse Ember?
GPT-5.6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Sol vs Muse Ember comparison based on?
9 benchmarks have a result for both models as of November 14, 2026. GPT-5.6 Sol's coding index rests on 5 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.